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L40S
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from collections.abc import Sequence
import random
from typing import Optional
import gradio as gr
import spaces
import torch
import transformers
# If the watewrmark is not detected, consider the use case. Could be because of
# the nature of the task (e.g., fatcual responses are lower entropy) or it could
# be another
_MODEL_IDENTIFIER = 'hf-internal-testing/tiny-random-gpt2'
_PROMPTS: tuple[str] = (
'prompt 1',
'prompt 2',
'prompt 3',
)
_CORRECT_ANSWERS: dict[str, bool] = {}
_TORCH_DEVICE = (
torch.device("cuda:0") if torch.cuda.is_available() else torch.device("cpu")
)
_WATERMARK_CONFIG = transformers.generation.SynthIDTextWatermarkingConfig(
ngram_len=5,
keys=[
654,
400,
836,
123,
340,
443,
597,
160,
57,
29,
590,
639,
13,
715,
468,
990,
966,
226,
324,
585,
118,
504,
421,
521,
129,
669,
732,
225,
90,
960,
],
sampling_table_size=2**16,
sampling_table_seed=0,
context_history_size=1024,
)
tokenizer = transformers.AutoTokenizer.from_pretrained(_MODEL_IDENTIFIER)
tokenizer.pad_token_id = tokenizer.eos_token_id
model = transformers.AutoModelForCausalLM.from_pretrained(_MODEL_IDENTIFIER)
model.to(_TORCH_DEVICE)
@spaces.GPU
def generate_outputs(
prompts: Sequence[str],
watermarking_config: Optional[
transformers.generation.SynthIDTextWatermarkingConfig
] = None,
) -> Sequence[str]:
tokenized_prompts = tokenizer(prompts, return_tensors='pt').to(_TORCH_DEVICE)
output_sequences = model.generate(
**tokenized_prompts,
watermarking_config=watermarking_config,
do_sample=True,
max_length=500,
top_k=40,
)
return tokenizer.batch_decode(output_sequences)
with gr.Blocks() as demo:
prompt_inputs = [
gr.Textbox(value=prompt, lines=4, label='Prompt')
for prompt in _PROMPTS
]
generate_btn = gr.Button('Generate')
with gr.Column(visible=False) as generations_col:
generations_grp = gr.CheckboxGroup(
label='All generations, in random order',
info='Select the generations you think are watermarked!',
)
reveal_btn = gr.Button('Reveal', visible=False)
with gr.Column(visible=False) as detections_col:
revealed_grp = gr.CheckboxGroup(
label='Ground truth for all generations',
info=(
'Watermarked generations are checked, and your selection are '
'marked as correct or incorrect in the text.'
),
)
detect_btn = gr.Button('Detect', visible=False)
def generate(*prompts):
standard = generate_outputs(prompts=prompts)
watermarked = generate_outputs(
prompts=prompts,
watermarking_config=_WATERMARK_CONFIG,
)
responses = standard + watermarked
random.shuffle(responses)
_CORRECT_ANSWERS.update({
response: response in watermarked
for response in responses
})
# Load model
return {
generate_btn: gr.Button(visible=False),
generations_col: gr.Column(visible=True),
generations_grp: gr.CheckboxGroup(
responses,
),
reveal_btn: gr.Button(visible=True),
}
generate_btn.click(
generate,
inputs=prompt_inputs,
outputs=[generate_btn, generations_col, generations_grp, reveal_btn]
)
def reveal(user_selections: list[str]):
choices: list[str] = []
value: list[str] = []
for response, is_watermarked in _CORRECT_ANSWERS.items():
if is_watermarked and response in user_selections:
choice = f'Correct! {response}'
elif not is_watermarked and response not in user_selections:
choice = f'Correct! {response}'
else:
choice = f'Incorrect. {response}'
choices.append(choice)
if is_watermarked:
value.append(choice)
return {
reveal_btn: gr.Button(visible=False),
detections_col: gr.Column(visible=True),
revealed_grp: gr.CheckboxGroup(choices=choices, value=value),
detect_btn: gr.Button(visible=True),
}
reveal_btn.click(
reveal,
inputs=generations_grp,
outputs=[
reveal_btn,
detections_col,
revealed_grp,
detect_btn
],
)
if __name__ == '__main__':
demo.launch()
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